Pdf Towards Synergistic Human Ai Collaboration In Hybrid Springer

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2134)) Included in the following conference series: A growing body of interdisciplinary literature indicates that human decision-making processes can be enhanced by Artificial Intelligence (AI). Nevertheless, the use of AI in critical domains has also raised significant concerns regarding its final users, those affected by the undertaken decisions, and the broader society. Consequently, recent studies are shifting their focus towards the development of human-centered frameworks that facilitate a synergistic human-machine collaboration while upholding ethical and legal standards. In this work, we present a taxonomy for hybrid decision-making systems to classify systems according to the type of interaction that occurs between human and artificial intelligence.

Furthermore, we identify gaps in the current body of literature and suggest potential directions for future research. This is a preview of subscription content, log in via an institution to check access. Tax calculation will be finalised at checkout Humanities and Social Sciences Communications volume 12, Article number: 821 (2025) Cite this article The integrating AI into teaching and learning has the potential to transform traditional classroom environments into hybrid intelligence learning environments, whereby human teachers and AI teachers (educational robots) work together synergistically to enhance students’... To understand and optimize the synergistic effect of human–AI collaboration in hybrid intelligence learning environments, this study proposes a human–AI synergy degree model (HAI-SDM).

A case study was conducted to examine the synergy degree and order degree in human–AI collaboration, involving forty students and one teacher from a class in a junior high school. The results indicate that the order degree between human teacher and AI machines remains at a moderate level while undergoing dynamic changes. The synergy degree fluctuates between low and moderate, reflecting relatively orderly development among the three subsystems (collaboration subject subsystem, collaboration process subsystem and collaboration environment subsystem), but one subsystem may exhibit disordered behaviours in... These findings have implications for developing more effective human-AI classroom collaboration and promoting the effective integration of AI into teaching and learning. The rapid development of artificial intelligence (AI) technology has brought unprecedented opportunities and challenges to the education sector. Particularly, under the impetus of human–AI collaboration, AI not only serves as an auxiliary tool to support teachers’ instructional tasks but also actively participates in classroom interactions in an intelligent manner, facilitating a profound...

2024; Zhou and Hou, 2024; Hilpert et al. 2023). Traditional educational models have limitations in facilitating personalized learning and optimizing resource allocation. Human–AI collaborative teaching offers an efficient and sustainable lens to address these challenges through close collaboration between AI systems and teachers (Díaz and Nussbaum, 2024; Chen et al. 2022). Human-AI collaboration in education, as an emerging interdisciplinary field, integrates cutting-edge theories and technologies from AI, education, cognitive science, and human–computer interaction.

The Hybrid Intelligence Learning Environments design aims to develop and implement effective human–AI collaboration in education. Its core philosophy lies in the seamless integration of human intelligence and machine intelligence to achieve optimized teaching outcomes through their synergistic collaboration (Cukurova, 2024; Bredeweg and Kragten, 2022). Within this environment, AI not only functions as an assistant to teachers but also plays a vital role in personalized learning (Mittal et al. 2024), real-time feedback (Weber et al. 2024), cognitive intervention (Fan et al. 2024), and emotional engagement (Järvelä et al.

2023). In recent years, researchers started to look into the design of educational robots, the application of AI in teaching, and the cognitive and behavioural dynamics of human–AI collaboration (Schecter et al. 2022; Niu et al. 2024; Wu et al. 2024). Existing studies have demonstrated significant advantages of human–AI collaboration in enhancing learning efficiency and supporting personalized learning (Huang et al.

2021). For instance, the integration of AI applications in real-time question answering (Fang et al. 2023) and homework grading (Duan et al. 2023) has effectively reduced teachers’ workload while improved instructional quality. The most extensive literature on AI applications in education focused on technological aspects, lacking systematic examination of human-AI collaboration during classroom instruction (Vössing et al. 2022; Yue and Li, 2023).

Given that effective human-AI collaboration relies on effective collaboration between humans and AI systems, investigating the effectiveness of collaboration and the degree of synergy in classrooms becomes crucial. To address this research gap, this study aims to develop a framework to evaluate and enhance the synergy and orderliness of human–AI collaboration in classrooms. Part of the book series: International Series on Computer, Entertainment and Media Technology ((ISCEMT)) Co-Creation and Augmentation: Human-AI collaboration offers unprecedented opportunities for co-creation and augmentation, where AI systems work alongside humans to enhance productivity and creativity. This chapter explores the various ways in which humans and AI can collaborate, highlighting real-world examples and case studies. It discusses the benefits of human-AI collaboration, from improved decision-making to innovative problem-solving.Ethical and Practical Considerations: While human-AI collaboration holds great promise, it also presents ethical and practical challenges.

This chapter examines the ethical considerations of human-AI collaboration, including issues of autonomy, accountability, and trust. It discusses practical strategies for ensuring that human-AI collaboration is conducted ethically and effectively, fostering a relationship that benefits both humans and AI systems. This is a preview of subscription content, log in via an institution to check access. Tax calculation will be finalised at checkout © 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 514))

Included in the following conference series: The evolution of Artificial Intelligence from traditional inference-based systems to sophisticated generative models has blurred the boundaries between machine and human capabilities, giving rise to Hybrid Intelligence (HI). HI represents a symbiotic relationship between human and artificial intelligence, integrating human wisdom and expertise with machine intelligence. This work aspires to explore the paradigm shift towards HI, with a focus on integrating human expertise with machine intelligence. It aims to address challenges in human-machine interaction and dynamic task management within HI systems, emphasizing the necessity for seamless integration to fully exploit the capabilities of both entities. Through interdisciplinary collaboration and empirical inquiry, this research endeavors to advance understanding and implementation of HI systems across diverse domains, paving the way for systems that harness the intelligence of humans and machines to...

This is a preview of subscription content, log in via an institution to check access. Tax calculation will be finalised at checkout This chapter offers a comprehensive overview of hybrid intelligence, through which humans collaborate with artificial intelligence (AI) systems to enhance human and AI capabilities while ensuring that human values, needs, and authority remain central. In line with the principles of Human-Centered AI (HCAI), hybrid intelligence leverages the complementary strengths of humans and AI to create systems that augment, rather than replace, human decision-making and creativity. The chapter discusses how hybrid intelligence prioritizes human oversight, controllability, authority, and ethical considerations, ensuring that AI serves to enhance human well-being and aligns with societal values. It also addresses recent technological advancements, including foundation models, which have highlighted the importance of hybrid intelligence in fields such as healthcare, decision support, and innovation.

Alongside these developments, the chapter emphasizes critical ethical and social challenges, such as fairness, accountability, trust, and privacy, within an HCAI framework. The chapter concludes by highlighting future research directions that integrate technical, social, and ethical perspectives to create sustainable, human-centered hybrid intelligence systems that prioritize human agency oversight as well as ethical design. This is a preview of subscription content, log in via an institution to check access. Abhivardhan. (2025). Data Governance.

In W. Xu (Ed.), Handbook of Human-Centered Artificial Intelligence (pp. 1–61). Springer. Allen, R. T., & Choudhury, P.

(2022). Algorithm-augmented work and domain experience: The countervailing forces of ability and aversion. Organization Science, 33(1), 149–169. https://doi.org/10.1287/orsc.2021.1554 Almatrafi, O., Johri, A., & Lee, H. (2024).

A systematic review of AI Literacy conceptualization, constructs, and implementation and assessment efforts (2019–2023). Computers and Education Open, 6, 100173.

People Also Search

• PDF Towards Synergistic Human-AI Collaboration in Hybrid ... - Springer
• Towards Synergistic Human-AI Collaboration in Hybrid ... - Springer
• Examining human-AI collaboration in hybrid intelligence learning ...
• Human-AI Collaboration: Synergies and Challenges - Springer
• Towards a Hybrid Intelligence Paradigm: Systematic Integration of Human ...
• PDF Hybrid intelligence - systematic approach and framework to ... - Springer
• PDF Understanding Human-AI Collaboration: A Systematic Review of ... - Springer
• PDF Examining human AI collaboration in hybrid intelligence learning ...
• PDF Human-ai Collaboration: Balancing Agentic Ai and Autonomy in Hybrid Systems
• Hybrid Intelligence - Springer